Quantifying Uncertainty in Potato Leaf Disease Detection: A Comparative Study of Deep Learning Models Using Monte Carlo Dropout

Abstract

In the face of ever-evolving challenges in agricultural disease management, particularly for the vital potato crop, this research endeavors to harness the power of Bayesian deep learning techniques for accurate and robust disease diagnosis. Drawing upon the Plant Village dataset, we developed and juxtaposed multiple models to discern the efficacy of uncertainty quantification using Monte Carlo Dropout (MC Dropout). Our comparative analysis across models emphasizes the profound impact of MC Dropout, underlining its superiority in enhancing model performance and reliability. The models enriched with MC Dropout not only demonstrated high diagnostic accuracy but also provided invaluable insights into prediction uncertainties, thereby bolstering the trustworthiness of the diagnosis. This study substantiates the promise of Bayesian methodologies in agricultural deep learning applications, laying the groundwork for future research that seeks to seamlessly merge precision with reliability in crop disease detection.